High Dynamic Range Video Quality Research Articles

Luminance decomposition and reconstruction for high dynamic range Video Quality Assessment

High dynamic range (HDR) video represents a wider range of brightness, detail and colour than standard dynamic range (SDR) video. However, SDR-based VQA (Video Quality Assessment) models struggle to capture HDR distortions. In addition, some of the existing methods designed for HDR video focus on emphasising the distortion of local areas of the video frame, ignoring the distortion of the video frame as a whole. Therefore, we propose a no reference VQA model based on luminance decomposition and recombination that provides excellent performance for HDR videos, called HDR-DRVQA. Specifically, HDR-DRVQA utilises a luminance decomposition strategy to decompose video frames into different regions for explicit extraction of perceptual features in different regions of the high dynamic range. We then further propose a residual aggregation module for recombining multi-region features to extract static spatial distortion representations and dynamic motion perception (captured by feature differences). Taking advantage of the Transformer network in remote dependency modelling, this information is fed into the Transformer network for interactive learning of motion perception and adaptively constructs a stream of spatial distortion information from shallow to deep layers during temporal aggregation. We validate that our model significantly outperforms SDR VQA and existing HDR VQA methods on the publicly available HDR databases.

A Study of Subjective and Objective Quality Assessment of HDR Videos.

As compared to standard dynamic range (SDR) videos, high dynamic range (HDR) content is able to represent and display much wider and more accurate ranges of brightness and color, leading to more engaging and enjoyable visual experiences. HDR also implies increases in data volume, further challenging existing limits on bandwidth consumption and on the quality of delivered content. Perceptual quality models are used to monitor and control the compression of streamed SDR content. A similar strategy should be useful for HDR content, yet there has been limited work on building HDR video quality assessment (VQA) algorithms. One reason for this is a scarcity of high-quality HDR VQA databases representative of contemporary HDR standards. Towards filling this gap, we created the first publicly available HDR VQA database dedicated to HDR10 videos, called the Laboratory for Image and Video Engineering (LIVE) HDR Database. It comprises 310 videos from 31 distinct source sequences processed by ten different compression and resolution combinations, simulating bitrate ladders used by the streaming industry. We used this data to conduct a subjective quality study, gathering more than 20,000 human quality judgments under two different illumination conditions. To demonstrate the usefulness of this new psychometric data resource, we also designed a new framework for creating HDR quality sensitive features, using a nonlinear transform to emphasize distortions occurring in spatial portions of videos that are enhanced by HDR, e.g., having darker blacks and brighter whites. We apply this new method, which we call HDRMAX, to modify the widely-deployed Video Multimethod Assessment Fusion (VMAF) model. We show that VMAF+HDRMAX provides significantly elevated performance on both HDR and SDR videos, exceeding prior state-of-the-art model performance. The database is now accessible at: https://live.ece.utexas.edu/research/LIVEHDR/LIVEHDR_index.html. The model will be made available at a later date at: https://live.ece.utexas.edu//research/Quality/index_algorithms.htm.

Inverse-tone-mapped HDR video quality assessment: A new dataset and benchmark

The accurate quality assessment for inverse-tone-mapped videos (ITMVs) has become increasingly important due to the ongoing rapid development of inverse tone mapping (ITM) algorithms. We observed that most existing video quality assessment (VQA) datasets and objective VQA methods are based on two underlying assumptions: the videos are in standard dynamic range (SDR) domain and the distortions are introduced in the video degradation process. However, ITMVs are videos in high dynamic range (HDR) domain, and are accompanied by distortions introduced during the enhancement process, which are distinct from those in degradation process. We then pose the question: Can existing VQA methods objectively assess the quality of ITMVs? To address this question and foster the advancement of VQA methods, we contribute the first publicly available dataset specifically tailored for assessing the quality of ITMVs. The dataset is called “VQA4ITM”, which is an acronym for “Video Quality Assessment for Inverse Tone Mapping”. Specifically, 30 distortion-free, professional 4K Rec. 2020 10 bit 50fps HDR source videos and 12 ITM methods were carefully selected, and a total of 360 ITMVs were generated in the end. A subjective experiment was conducted in a professional HDR viewing environment and 6,098 valid quality scores were finally gathered to calculate the mean opinion score (MOS) of each ITMV. Furthermore, we perform benchmark experiments on the VQA4ITM dataset, revealing that existing VQA methods are unable to fairly assess the quality of ITMVs. The inadequate performance of existing VQA methods on the VQA4ITM dataset highlights the challenging nature of our dataset, as well as the imperative need for the development of more effective VQA algorithms in the context of ITM. To facilitate future research, the VQA4ITM dataset will be made publicly accessible.

HDR-BVQM: High dynamic range blind video quality model

Considerable progress has been made toward developing standard dynamic range (SDR) blind video quality assessment (BVQA) models that do not require any baseline reference for quality prediction. However, there is no such method for the high dynamic range (HDR) content. Unlike SDR video, HDR video represents a high-fidelity representation of the real-world scene by preserving the wide luminance range and color gamut. Therefore, SDR BVQA models are not suitable for HDR BVQA. Towards ameliorating this, a first-of-its-kind BVQA model for HDR content is presented in this work. The proposed HDR blind video quality model (HDR-BVQM) is inspired by the spatio-temporal natural scene statistics model, previously employed in SDR blind quality assessment metrics. To build our proposed model, we first develop a comprehensive subjective HDR video quality dataset, including 228 distorted videos generated through three different (H.264, HEVC, packet drop) distortion processes from 19 pristine HDR videos. The developed dataset is then used to extract HDR relevant features, which vary in different distortion types, to train and test the proposed HDR-BVQM. The features are based on the pointwise, pairwise log-derivative, and motion coherence based statistics. Finally, detailed validation and performance comparison is performed with full-reference HDR and no-reference SDR quality assessment methods. The results reveal that the quality prediction by HDR-BVQM correlates with the human judgment of quality.

HDR video quality assessment: Perceptual evaluation of compressed HDR video

Compared with standard dynamic range (SDR) video, the high dynamic range (HDR) video can provide us significantly enhanced viewing experience. In particular, compared to SDR video, the HDR video has better contrast and preserves more details for the same scene. With the rapid development of HDR video compression technology, there is a lack of trusted quality measure of HDR video compression. In order to facilitate the future development of objective HDR quality assessment, we build a HDR video quality assessment database, in which the bitstream is created by compressing a series of HDR video sequences. In the compression, the quantization parameters (QP) are set to 12 levels according to the configuration of the codec. The subjective quality of each bitstream is rated by 22 viewers. It is revealed that the subject viewers have arrived at a reasonable agreement on the subjective quality of different QP levels. This paper presents the results of subjective quality assessment of HDR compressed video, which also exhibits that there is significant room to further improve the objective HDR video quality assessment algorithms.

HDR-VQM: An objective quality measure for high dynamic range video

High dynamic range (HDR) signals fundamentally differ from the traditional low dynamic range (LDR) ones in that pixels are related (proportional) to the physical luminance in the scene (i.e. scene-referred). For that reason, the existing LDR video quality measurement methods may not be directly used for assessing quality in HDR videos. To address that, we present an objective HDR video quality measure (HDR-VQM) based on signal pre-processing, transformation, and subsequent frequency based decomposition. Video quality is then computed based on a spatio-temporal analysis that relates to human eye fixation behavior during video viewing. Consequently, the proposed method does not involve expensive computations related to explicit motion analysis in the HDR video signal, and is therefore computationally tractable. We also verified its prediction performance on a comprehensive, in-house subjective HDR video database with 90 sequences, and it was found to be better than some of the existing methods in terms of correlation with subjective scores (for both across sequence and per sequence cases). A software implementation of the proposed scheme is also made publicly available for free download and use.